Identifying Mubasher software products through sentiment analysis of Arabic tweets

AL-Rubaiee, Hamed Saad; Qiu, Renxi; Li, Dayou

doi:10.1109/iccsii.2016.7462396

Cited by 37 publications

(38 citation statements)

References 19 publications

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“…In [9], the authors proposed a hybrid approach which combines SVM and semantic orientation on Egyptian dialect corpus of tweets. In [10], the authors presented a model for sentiment analysis of Saudi Arabic tweets to extract feedback from Mubasher products. In [11], the authors developed Corpus for Arabic Sentiment Analysis of Saudi Tweets.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Atoum¹,

Nouman²

2019

ijacsa

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Sentiment Analysis (SA) of social media contents has become one of the growing areas of research in data mining. SA provides the ability of text mining the public opinions of a subjective manner in real time. This paper proposes a SA model of Arabic Jordanian dialect tweets. Tweets are annotated on three different classes; positive, negative, and neutral. Support Vector Machines (SVM) and Naïve Bayes (NB) are used as supervised machine learning classification tools. Preprocessing of such tweets for SA is done via; cleaning noisy tweets, normalization, tokenization, namely, Entity Recognition, removing stop words, and stemming. The results of the experiments conducted on this model showed encouraging outcomes when Arabic light stemmer/segment is applied on Arabic Jordanian dialect tweets. Also, the results showed that SVM has better performance than NB on such tweets' classifications.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Atoum¹,

Nouman²

2019

ijacsa

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“…Al-Rubaiee et al [13] explored the preprocessing steps within RapidMiner; normalization, tokenization, stop word removal, and stemming. It was demonstrated that text preprocessing is a key factor in sentiment classification and shows different levels of accuracy by creating N-grams term of tokens.…”

Section: Discussion and Future Research Avenuesmentioning

confidence: 99%

“…Preprocessing [17] Normalization, POS tagging [24][25][26][27] Stemming [28][29][30][31][32][33] Text cleaning [34][35][36][37][38][39] Normalization, stemming, stop words removal [40][41][42] Text cleaning, normalization, stemming, stop words removal [43][44][45] Normalization Text cleaning, normalization, tokenization, stemming, stop words removal [49][50][51][52] Normalization, tokenization [53,54] Text cleaning, normalization, tokenization [55,56] Normalization, tokenization, POS tagging [13,[57][58][59][60][61][62][63][64] Normalization, tokenization, stemming, stop words removal [65,66] Normalization, tokenization, stemming, lemmatization [67,68] Text cleaning, normalization, tokenization, stemming [69] Text cleaning, tokenization, stemming, negation detection [70]…”

Section: Referencementioning

confidence: 99%

“…Since its introduction since a decade, ASA has become one of the most popular forms of information extraction from the reviews. ese reviews contributed in many benefits, such as showing the product brand or service valuable insights [12][13][14][15], identifying potential product advocates or social media influencers [16][17][18][19], and detecting e-mail spam [20]. Consequently, ASA has been studied in various contexts, and a large number of studies has been published on the topic.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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“…On the contrary, NB is not sensitive to unbalanced data sets. Authors of [15] performed sentiment classification by two forms sentiment, polarity classification, and rating classification. They applied machine learning using SVM, MNB, and BNB.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Sentiment Analysis in Arabic Using Document Level

Awajan¹,

Mohamad²

2018

IJET

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With the rapid increase of web contents and the spread of social media and microblogs such as Twitter and etc.., the focus on the sentiment analysis (SA) is deeply being studied. The research on Arabic sentiment analysis is progressing very slow in compared to English sentiment analysis. The former has recently attracted a considerable concentration of researchers. In this respect, this paper aims to present a brief review of some major works that have addressed the document-level sentiment analysis in Arabic. This review includes research and studies published during the period of 2011-2017 for different approaches of document-level sentiment analysis.

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Identifying Mubasher software products through sentiment analysis of Arabic tweets

Cited by 37 publications

References 19 publications

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Arabic Sentiment Analysis: A Systematic Literature Review

A Review on Sentiment Analysis in Arabic Using Document Level

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